Competition-based model of pheromone component ratio detection in the moth

For some moth species, especially those closely interrelated and sympatric, recognizing a specific pheromone component concentration ratio is essential for males to successfully locate conspecific females. We propose and determine the properties of a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for the ratio of binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. An exception to this rule is that it is beneficial if connections between generalist LNs (i.e. excited by either pheromone component) and specialist LNs (i.e. excited by one component only) have the same strength as the reciprocal specialist to generalist connections. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which, in contrast to expectations with a population rate code, leads to a broadening of responses for higher overall concentrations consistent with experimental observations. (3) when longer durations of the competition between LNs were observed it did not lead to higher recognition accuracy

Wide Field Imaging. I. Applications of Neural Networks to object detection and star/galaxy classification

[Abriged] Astronomical Wide Field Imaging performed with new large format CCD detectors poses data reduction problems of unprecedented scale which are difficult to deal with traditional interactive tools. We present here NExt (Neural Extractor): a new Neural Network (NN) based package capable to detect objects and to perform both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first discriminated from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold and then they are classified as stars or as galaxies through diagnostic diagrams having variables choosen accordingly to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of "what an object is" (id est, it keeps all structures composed by more than one pixels) and performs the detection via an unsupervised NN approaching detection as a clustering problem which has been thoroughly studied in the artificial intelligence literature. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features, we use a NN to select the most significant features among the large number of measured ones, and then we use their selected features to perform the classification task. In order to optimise the performances of the system we implemented and tested several different models of NN. The comparison of the NExt performances with those of the best detection and classification package known to the authors (SExtractor) shows that NExt is at least as effective as the best traditional packages.Comment: MNRAS, in press. Paper with higher resolution images is available at http://www.na.astro.it/~andreon/listapub.htm

Neural Nets and Star/Galaxy Separation in Wide Field Astronomical Images

One of the most relevant problems in the extraction of scientifically useful information from wide field astronomical images (both photographic plates and CCD frames) is the recognition of the objects against a noisy background and their classification in unresolved (star-like) and resolved (galaxies) sources. In this paper we present a neural network based method capable to perform both tasks and discuss in detail the performance of object detection in a representative celestial field. The performance of our method is compared to that of other methodologies often used within the astronomical community.Comment: 6 pages, to appear in the proceedings of IJCNN 99, IEEE Press, 199

A study of early stopping, ensembling, and patchworking for cascade correlation neural networks

The constructive topology of the cascade correlation algorithm makes it a popular choice for many researchers wishing to utilize neural networks. However, for multimodal problems, the mean squared error of the approximation increases significantly as the number of modes increases. The components of this error will comprise both bias and variance and we provide formulae for estimating these values from mean squared errors alone. We achieve a near threefold reduction in the overall error by using early stopping and ensembling. Also described is a new subdivision technique that we call patchworking. Patchworking, when used in combination with early stopping and ensembling, can achieve an order of magnitude improvement in the error. Also presented is an approach for validating the quality of a neural network’s training, without the explicit use of a testing dataset

StarGO: A New Method to Identify the Galactic Origins of Halo Stars

We develop a new method StarGO (Stars' Galactic Origin) to identify the galactic origins of halo stars using their kinematics. Our method is based on self-organizing map (SOM), which is one of the most popular unsupervised learning algorithms. StarGO combines SOM with a novel adaptive group identification algorithm with essentially no free parameters. In order to evaluate our model, we build a synthetic stellar halo from mergers of nine satellites in the Milky Way. We construct the mock catalogue by extracting a heliocentric volume of 10 kpc from our simulations and assigning expected observational uncertainties corresponding to bright stars from Gaia DR2 and LAMOST DR5. We compare the results from StarGO against that from a Friends-of-Friends (FoF) based method in the space of orbital energy and angular momentum. We show that StarGO is able to systematically identify more satellites and achieve higher number fraction of identified stars for most of the satellites within the extracted volume. When applied to data from Gaia DR2, StarGO will enable us to reveal the origins of the inner stellar halo in unprecedented detail.Comment: 11 pages, 7 figures, Accepted for publication in Ap